Evaluating Intervention Reporting in Nursing Journal <scp>RCTs</scp> Using the <scp>TIDieR</scp> Checklist: A Cross‐Sectional Study
Bibliographic record
Abstract
AIM: To assess the completeness of intervention reporting in randomised controlled trials (RCTs) published in nursing journals based on the Template for Intervention Description and Replication (TIDieR) checklist. DESIGN: A cross-sectional study. METHODS: RCTs published in English in nursing journals between January 2022 and December 2022 were identified through PubMed. Title- and abstract-screening were undertaken independently by two reviewers to select eligible trials, from which data were extracted. Reports of interventions were likewise independently evaluated based on the TIDieR checklist. Binary logistic regression analysis was performed to investigate potential predictors for the compliance of TIDieR. RESULTS: Our analysis included 303 eligible trials, which generally adhered to most items on the TIDieR checklist, though adherence varied across the trials. Slightly fewer than half of the trials demonstrated good reporting quality. Poor reporting was associated in areas such as modifications, tailoring, and the type of locations where the intervention occurred. Additionally, suboptimal reporting on intervention adherence was noted. Compliance with TIDieR was found to be influenced by factors such as funding availability and the journal's ranking. CONCLUSIONS: Our study revealed suboptimal reporting of the TIDieR items in RCTs published in nursing journals. More rigorous adherence to the TIDieR checklist is needed to improve the quality of intervention reporting. Additionally, comparing adherence before and after the implementation of TIDieR may be considered in future investigations. IMPACT: This paper represents the first study to appraise the reporting quality of RCTs in nursing journals based on the TIDieR checklist. Evidence of suboptimal compliance of RCTs to the TIDieR checklist items is presented. PATIENT OR PUBLIC CONTRIBUTIONS: No patient or public contribution applied.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.678 | 0.860 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".